Meteorological private cloud information security assessment method based on distributed architecture

Abstract

The invention relates to the technical field of information security processing, and particularly discloses a meteorological private cloud information security assessment method based on a distributed architecture, the method comprises the steps of data acquisition, data processing, data storage and security assessment, the security degree is acquired by assessing the data of acquisition equipment, the integrity and confidentiality of the data are ensured, and the security of the data is improved. The method comprises the following steps: matching a data processing mode of meteorological data, obtaining and analyzing data processing information of the meteorological data, obtaining a meteorological data storage category through matching of a meteorological private cloud information platform, evaluating the data storage security degree of the meteorological private cloud information platform, and comparing the data storage security degree with a preset data storage security threshold value. By means of the method, potential safety risks in the data storage process can be found in time, safety evaluation of the meteorological data is completed, it can be ensured that the meteorological data is always kept within a safety range in the collection, transmission and storage processes, and therefore the authenticity of the data is guaranteed, meteorological data errors and abnormal values are reduced, and the data quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information security processing, and in particular to a meteorological private cloud information security assessment method based on a distributed architecture. Background Art

[0002] The current information security processing technology solutions may focus on technical-level security assessments while ignoring the security risks associated with the use of equipment, resulting in the inability to cover all potential security threats and vulnerabilities during information security processing. At the same time, traditional information security processing technology solutions often deploy different parts of data processing on one server rather than on multiple servers in a distributed manner, which increases the error rate of information security processing and also means that if a node or server fails, other nodes will not be able to provide services, thereby reducing the continuity and stability of meteorological information security assessments.

[0003] For example, the invention patent with announcement number CN114417565B discloses a method for modeling a tropospheric refractive index gradient profile based on ground meteorological data, comprising the following steps: Step 1, preprocessing the historical meteorological data of a given sounding site to obtain profile data of tropospheric meteorological parameters and refractive index gradient; Step 2, establishing an expression equation for a segmented model of a refractive index gradient profile; Step 3, combining the equation for the change of meteorological parameters with height to obtain an expression of a refractive index gradient model based on ground meteorological parameters; Step 4, establishing a segmented model of a refractive index gradient profile based on ground meteorological data; The method for modeling a tropospheric refractive index gradient profile based on ground meteorological data disclosed in the above application can obtain the refractive index gradient profile above the site according to the ground meteorological data of the site, and can provide technical support for the evaluation of radio wave propagation effects and radio system design in the fields of measurement and control radar design, satellite navigation positioning, precise geodetic measurement, astronomical measurement, etc., and has good practicality.

[0004] For example, the invention patent with announcement number CN114662344B proposes an atmospheric pollution source tracing and prediction method based on continuous online observation data, which includes: constructing a grid model within a preset area and preprocessing the grid model; constructing a high-precision small-scale meteorological prediction model, using the meteorological grid data within the preset area as input data of the high-precision small-scale meteorological prediction model, simulating meteorological factors in combination with the underlying surface characteristics, and calculating high-precision three-dimensional spatial gridded meteorological element data; constructing an atmospheric pollution diffusion model, inputting the high-precision three-dimensional spatial gridded meteorological element data into the atmospheric pollution diffusion model, and performing reverse deduction simulation of the meteorological field and the diffusion field in time to obtain the probability of the comprehensive impact of the emission source on the observation site in time and space, and finally determining whether the emission source is a known source; the above application can achieve accurate tracing effect and meet the needs of actual applications.

[0005] Combining the above-mentioned technical solutions, it is found that with the increasing importance of information security, it is necessary to organize more secure, accurate and efficient information security processing technical solutions. However, most information security processing technical solutions only process the data with the corresponding software, which results in a high error rate in data security processing, leading to deviations in the judgment of information security, and ultimately affecting the use of information. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a meteorological private cloud information security assessment method based on a distributed architecture, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a meteorological private cloud information security assessment method based on a distributed architecture, including: data acquisition: monitoring the data acquisition process of the collection device, evaluating the data acquisition security of the collection device, and if the data acquisition security of the collection device is greater than or equal to the preset data acquisition security threshold, matching the data processing method of the meteorological data; data processing: based on the data processing process of the meteorological data, acquiring and analyzing the data processing information of the meteorological data, and obtaining the meteorological data storage category through matching the meteorological private cloud information platform; data storage: storing the meteorological data in the meteorological private cloud information platform through the meteorological data storage category, and obtaining data storage related information, and evaluating the data storage security of the meteorological private cloud information platform; security assessment: comparing the data storage security of the meteorological private cloud information platform with the preset data storage security threshold, thereby issuing an early warning prompt for the storage security of the meteorological private cloud information platform, and finally completing the security assessment of the meteorological data.

[0008] As a further method, the security assessment of meteorological data is completed, and the specific analysis process is: comparing the data storage security of the meteorological private cloud information platform with a preset data storage security threshold. If the data storage security of the meteorological private cloud information platform is less than the data storage security threshold, an early warning prompt is issued for the storage security of the meteorological private cloud information platform, thereby completing the security assessment of meteorological data.

[0009] As a further method, the data storage related information specifically includes the data transmission rate of the meteorological private cloud information platform at each storage time point, the number of data backup times of the meteorological private cloud information platform within the storage period, and the data encryption strength value of the meteorological private cloud information platform.

[0010] As a further method, the data storage security of the meteorological private cloud information platform is specifically analyzed as follows: the data transmission rate of the meteorological private cloud information platform at each storage time point is averaged to obtain the average data transmission rate of the meteorological private cloud information platform within the storage period; the number of data backups of the meteorological private cloud information platform within the storage period is ratioed with the duration corresponding to the storage period to obtain the data backup frequency of the meteorological private cloud information platform within the storage period; the data transmission limit rate, data backup limit frequency, and data encryption strength limit value are extracted from the meteorological cloud information database.

[0011] As a further method, the matching obtains the meteorological data storage category, and the specific analysis process is: the data processing information of the meteorological data includes basic calculations on each key data point in the meteorological data to obtain the data processing set of the meteorological data; the data processing set of the meteorological data is matched with the data storage category corresponding to each data processing set predefined in the meteorological private cloud information platform to obtain the meteorological data storage category.

[0012] As a further method, the data processing method for matching meteorological data is specifically matched as follows: the data acquisition security of the collection device is compared with a preset data acquisition security threshold; if the data acquisition security of the collection device is greater than or equal to the data acquisition security threshold, the data acquisition security of the collection device is matched with the data processing method corresponding to each data acquisition security interval, so as to match the data processing method of the meteorological data collected in the collection device.

[0013] As a further method, the data acquisition safety of the acquisition device is specifically analyzed as follows: by monitoring the data acquisition process of the acquisition device, the data acquisition related information of the acquisition device is acquired; the data acquisition related information of the acquisition device specifically includes the ambient humidity of the data acquisition area at each acquisition time point, the wind speed collected by the acquisition device at each acquisition time point, and the number of acquisition errors of the acquisition device within the acquisition cycle; the ambient humidity of the data acquisition area at each acquisition time point is subjected to difference processing with the preset suitable operating humidity of the acquisition device to obtain the humidity deviation value of the data acquisition area at each acquisition time point, and the values ​​are arranged in order from large to small, and the humidity deviation value ranked first is recorded as the maximum humidity deviation value of the data acquisition area within the acquisition cycle; the actual wind speed of the data acquisition area at each acquisition time point is obtained, and the wind speed collected by the acquisition device at each acquisition time point is subjected to difference processing to obtain the wind speed collection difference of the acquisition device at each acquisition time point, and the wind speed collection reference difference is subjected to ratio processing to obtain the collection accuracy of the acquisition device at each acquisition time point; the suitable operating humidity and wind speed collection reference difference of the acquisition device are extracted from the meteorological cloud information library.

[0014] The second aspect of the present invention provides a system for a meteorological private cloud information security assessment method based on a distributed architecture, including: a data acquisition module, which is used to monitor the data acquisition process of a collection device and assess the data acquisition security of the collection device. If the data acquisition security of the collection device is greater than or equal to a preset data acquisition security threshold, a data processing method for the meteorological data is matched; a data processing module, which is used to acquire and analyze data processing information of the meteorological data based on the data processing process of the meteorological data, and obtain a meteorological data storage category through matching on a meteorological private cloud information platform; a data storage module, which is used to store the meteorological data in the meteorological private cloud information platform through the meteorological data storage category, and obtain data storage related information to assess the data storage security of the meteorological private cloud information platform; a security assessment module, which is used to compare the data storage security of the meteorological private cloud information platform with a preset data storage security threshold, thereby providing an early warning for the storage security of the meteorological private cloud information platform, and ultimately completing the security assessment of the meteorological data.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0016] (1) The present invention provides a meteorological private cloud information security assessment method based on a distributed architecture. By assessing the data acquisition security of the acquisition equipment, potential loopholes can be identified and resolved, and the possibility of data being tampered with, stolen or illegally accessed during the acquisition process can be prevented, thereby ensuring the integrity and confidentiality of the data, matching the data processing method of the meteorological data, acquiring and analyzing the data processing information of the meteorological data, obtaining the meteorological data storage category through matching the meteorological private cloud information platform, assessing the data storage security of the meteorological private cloud information platform, and comparing it with the preset data storage security threshold, the potential security risks in the data storage process can be discovered in a timely manner, thereby completing the security assessment of the meteorological data, and ensuring that the meteorological data is always kept within a safe range during the acquisition, transmission and storage process, thereby ensuring the authenticity of the data, reducing meteorological data errors and outliers, and improving data quality.

[0017] (2) The present invention stores meteorological data in the meteorological private cloud information platform through the meteorological data storage category, which effectively enhances the security and privacy protection of meteorological data, obtains data storage related information, evaluates the data storage security of the meteorological private cloud information platform, greatly reduces the risk of meteorological data leakage, and ensures the reliability and availability of meteorological data.

[0018] (3) The present invention achieves redundancy and fault tolerance in assessing the security of meteorological data by distributing meteorological data on multiple servers, which means that even if a node or server fails, other nodes can continue to provide services, thereby ensuring the continuity and stability of meteorological data security assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0020] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0021] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0022] Figure 3 It is a line graph of data transmission rate variation involved in the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] Reference Figure 1 As shown, the first aspect of the present invention provides a meteorological private cloud information security assessment method based on a distributed architecture, including: data acquisition: monitoring the data acquisition process of the collection device, evaluating the data acquisition security of the collection device, and if the data acquisition security of the collection device is greater than or equal to a preset data acquisition security threshold, then matching the data processing method of the meteorological data.

[0025] It should be explained that the above-mentioned distributed architecture is applied in the meteorological private cloud information platform, which significantly enhances the continuity and stability of the meteorological data security assessment system. The system subdivides the meteorological data assessment process into three key links: data acquisition, data processing, and data storage through a carefully designed data assessment process. The meteorological private cloud information platform adopts a distributed architecture design, which usually includes the hardware device layer, the basic software resource layer, and the resource management interface layer from bottom to top. The hardware device layer is used for data acquisition, the basic software resource layer is used for data processing, and the resource management interface layer is used for data storage. This process not only ensures the comprehensiveness and accuracy of the data, but also realizes the efficiency and flexibility of the assessment process.

[0026] In the distributed deployment mode, meteorological data is intelligently distributed to multiple servers. Each server undertakes specific processing tasks, including comprehensive and real-time information capture, precise and intelligent information extraction, and safe and efficient information management. Each link is deeply optimized and highly integrated, thus achieving optimal resource allocation and load balancing. This deployment method not only improves the system's processing capabilities, but also enhances its fault tolerance and scalability. Even if a server fails, other servers can quickly take over its work to ensure the continuous operation of the entire system.

[0027] Specifically, the data acquisition security of the acquisition device is analyzed in the following steps:

[0028] By monitoring the data acquisition process of the acquisition device, the data acquisition related information of the acquisition device is obtained.

[0029] The above-mentioned collection equipment refers to the equipment required to collect various meteorological elements in real time and accurately. In this embodiment, the collection equipment includes but is not limited to a temperature sensor, a wind speed sensor, and a collection counter.

[0030] The data acquisition related information of the acquisition device specifically includes the ambient humidity of the data acquisition area at each acquisition time point, wherein the data acquisition area refers to the area where the acquisition device collects meteorological data, the wind speed collected by the acquisition device at each acquisition time point, and the number of acquisition errors of the acquisition device during the acquisition cycle.

[0031] The above-mentioned acquisition time points are various acquisition time points divided by the acquisition cycle. The acquisition cycle is determined by the data processing and monitoring personnel based on a comprehensive analysis of comprehensive factors such as the regional environment, actual needs, and equipment status.

[0032] The above-mentioned environmental humidity can be collected by a temperature sensor in the collection device; the wind speed can be collected by a wind speed sensor in the collection device; and the number of collection errors can be collected by a collection counter in the collection device.

[0033] The ambient humidity of the data acquisition area at each acquisition time point is subtracted from the suitable operating humidity of the preset acquisition equipment to obtain the humidity deviation value of the data acquisition area at each acquisition time point. The humidity deviation value is absolute and arranged in order from large to small. The humidity deviation value ranked first is recorded as the maximum humidity deviation value of the data acquisition area during the acquisition period.

[0034] The actual wind speed in the data acquisition area at each acquisition time point is obtained, and the difference processing is performed with the wind speed collected by the acquisition device at each acquisition time point to obtain the wind speed collection difference of the acquisition device at each acquisition time point, and the ratio processing is performed with the predefined wind speed collection reference difference to obtain the collection accuracy of the collection device at each acquisition time point.

[0035] The actual wind speed in the above data acquisition area at each acquisition time point can be measured by an anemometer.

[0036] In this embodiment, the wind speed collection difference of the above-mentioned collection equipment at each acquisition time point is shown in Table 1:

[0037] Table 1 The relationship between the collected wind speed and the actual wind speed

[0038]

[0039]

[0040] In this embodiment, according to the table of wind speed collection differences of the above-mentioned collection equipment at each acquisition time point, it can be learned that the wind speed collected by the collection equipment at acquisition time point 3 is the same as the actual wind speed in the data acquisition area at acquisition time point 3. This may be because at this acquisition time point, the collection state of the collection equipment is relatively ideal or stable; the wind speed collected by the collection equipment at acquisition time point 4 is significantly different from the actual wind speed in the data acquisition area at acquisition time point 4, indicating that under the influence of different factors such as temperature and humidity, the collection differences of the collection equipment may be quite significant.

[0041] The reference difference of suitable operating humidity and wind speed for the collection equipment is extracted from the meteorological cloud information database.

[0042] The data stored in the above-mentioned meteorological cloud information library is obtained by fitting the processing data of statistically analyzing multiple meteorological data. For example, the suitable operating humidity of the collection equipment is obtained by obtaining the environmental humidity when the collection equipment is running multiple times, and the data processing and monitoring personnel determine the humidity that meets the best operating effect of the collection equipment under different humidities, and perform average processing on it. The humidity is used as the suitable operating humidity of the collection equipment. In this way, the data in the meteorological cloud information library is obtained, and the value may change due to the different collection area environment, actual needs, and collection equipment status. This embodiment does not make any special limitations on this.

[0043] Furthermore, the data acquisition safety of the acquisition device is obtained by comprehensive analysis of the maximum humidity deviation value of the data acquisition area within the acquisition period, the acquisition accuracy of the acquisition device at each acquisition time point, and the number of acquisition errors of the acquisition device within the acquisition period in this embodiment, so as to determine the data acquisition safety of the acquisition device. In this embodiment, a more accurate calculation method is used to obtain the safety, and the specific expression is:

[0044]

[0045] Wherein A is the data acquisition safety of the acquisition device. In this embodiment, if a larger data acquisition safety of the acquisition device is to be obtained, it is necessary to conduct a comprehensive analysis of the humidity of the data acquisition area, the acquisition accuracy of the acquisition device, and the number of acquisition errors of the acquisition device. When the ambient humidity of the data acquisition area at each acquisition time point is high and higher than the preset suitable operating humidity of the acquisition device, the maximum deviation value of the humidity in the data acquisition area during the acquisition cycle is large, so that the normal operation of the acquisition device is affected, resulting in a decrease in device performance, thereby affecting the acquisition accuracy of the acquisition device and reducing the acquisition accuracy of the acquisition device. At the same time, under extreme humidity conditions, when the acquisition device cannot work normally, it will cause data loss or abnormal values ​​of the acquisition device, so as to increase the number of acquisition errors of the acquisition device. These negative effects will cause harm to the data acquisition of the acquisition device, so as to reduce the data acquisition safety of the acquisition device.

[0046] ΔW is the maximum deviation of humidity in the data acquisition area during the acquisition cycle, which refers to the maximum difference between the ambient humidity in the data acquisition area at each acquisition time point and the preset suitable operating humidity of the acquisition equipment.

[0047] ZQ i It is the collection accuracy of the collection device at each acquisition time point, which refers to the ratio between the wind speed collection difference of the collection device at each acquisition time point and the predefined wind speed collection reference difference.

[0048] i represents the number of each acquisition time point, i=1, 3, 4, ..., m, and m is the total number of acquisition time points.

[0049] CS is the number of acquisition errors of the acquisition device during the acquisition cycle, which refers to the total number of errors or failures that occur when the acquisition device attempts to collect data during the acquisition cycle. These errors may be caused by various factors, including but not limited to the following aspects: acquisition device failure, sensor problem, signal interference, and configuration error.

[0050] D1 is the impact factor corresponding to the unit value of the maximum humidity deviation value predefined in the meteorological cloud information database.

[0051] In a specific embodiment, a mapping set of the maximum humidity deviation value and its corresponding influencing factor is constructed based on the relationship between the historical maximum humidity deviation value and the data acquisition safety of the collection device, and the real-time maximum humidity deviation value is input into the mapping set to obtain the influencing factor corresponding to the unit value of the maximum humidity deviation value. At the same time, in this example, its value range is (0, 1).

[0052] D2 is the impact factor corresponding to the unit value of acquisition accuracy predefined in the meteorological cloud information database.

[0053] In a specific embodiment, a mapping set of collection accuracy and its corresponding influencing factors is constructed based on the relationship between historical collection accuracy and the data acquisition security of the collection device, and the real-time collection accuracy is input into the mapping set to obtain the influencing factor corresponding to the unit value of the collection accuracy. At the same time, its value range in this example is (0, 1).

[0054] D3 is the impact factor corresponding to a single acquisition error predefined in the meteorological cloud information database.

[0055] In a specific embodiment, a mapping set of the number of collection errors and their corresponding impact factors is constructed based on the relationship between the number of historical collection errors and the data acquisition safety of the collection device, and the real-time number of collection errors is input into the mapping set to obtain the impact factor corresponding to a single collection error. At the same time, its value range in this example is (0, 1).

[0056] Specifically, the data processing method for matching the meteorological data includes the following specific matching process:

[0057] The data acquisition security of the collection device is compared with the data acquisition security threshold preset in the meteorological cloud information library. If the data acquisition security of the collection device is greater than or equal to the data acquisition security threshold, the data acquisition security of the collection device is matched with the data processing method corresponding to each data acquisition security interval. The specific matching process is: obtain a mapping set of the data acquisition security and data processing method of the collection device from the meteorological cloud information library, first determine the interval to which the data acquisition security of the collection device belongs, and assign the data processing method corresponding to the interval to the collection device corresponding to the data acquisition security, so as to match the data processing method of the meteorological data collected in the collection device.

[0058] The data processing methods of the above-mentioned meteorological data include but are not limited to mean processing, maximum processing, difference processing, and ratio processing of the meteorological data.

[0059] If the data acquisition security of the collection device is less than the data acquisition security threshold, it means that there are security hazards or risks in the process of collecting relevant meteorological data, and the preset security standards are not met. In this case, it is necessary to conduct a security audit on the collection device. By obtaining the security log of the collection device, the existing security vulnerabilities and potential threats can be identified, and corresponding measures can be taken to repair the corresponding harmful vulnerabilities, so as to finally make the data acquisition security of the collection device greater than or equal to the data acquisition security threshold.

[0060] Data processing: Based on the data processing process of meteorological data, obtain and analyze the data processing information of meteorological data, and match the meteorological data storage category through the meteorological private cloud information platform.

[0061] The data processing process of the above meteorological data is specifically to perform mean processing on the meteorological data.

[0062] Furthermore, the matching obtains the meteorological data storage category, and the specific analysis process is as follows:

[0063] The data processing information of the meteorological data includes performing basic calculations on each key data point in the meteorological data to obtain a data processing set of the meteorological data.

[0064] The above-mentioned key data points refer to multiple data in the meteorological data, including the maximum humidity deviation value of the data acquisition area during the acquisition period, the acquisition accuracy of the acquisition equipment at each acquisition time point, and the number of acquisition errors of the acquisition equipment during the acquisition period.

[0065] The above basic calculations refer to the average calculation, median calculation, and standard deviation calculation of each key data point in the meteorological data.

[0066] The data processing set of meteorological data is matched with the data storage categories corresponding to each data processing set predefined in the meteorological private cloud information platform. The specific matching process is: obtain the mapping set of the data processing set of meteorological data and the data storage category from the meteorological cloud information library, first determine the category of the data processing set of meteorological data, and assign the data storage category corresponding to the category to the meteorological data corresponding to the data processing set, so as to match the meteorological data storage category.

[0067] Data storage: Meteorological data is stored in the meteorological private cloud information platform through the meteorological data storage category, and data storage related information is obtained to evaluate the data storage security of the meteorological private cloud information platform.

[0068] The above-mentioned meteorological private cloud information platform is a cloud platform specially built for the meteorological business platform. It integrates cloud computing services and has the characteristics of security and reliability. It uses the distributed storage, joint processing, network resource storage and other capabilities of cloud computing to realize real-time processing, storage, analysis and collection of meteorological data.

[0069] Specifically, the data storage-related information includes the data transmission rate of the meteorological private cloud information platform at each storage time point, the number of data backups of the meteorological private cloud information platform during the storage period, and the data encryption strength value of the meteorological private cloud information platform.

[0070] The above storage time points are divided into storage time points by storage cycles. The determination of the storage cycle is obtained by comprehensive analysis by data processing monitoring personnel based on comprehensive factors such as data processing information, actual needs, storage status, etc.

[0071] The above data transmission rate, data backup times and data encryption strength values ​​can all be obtained through the data storage implementation report table of the meteorological private cloud information platform.

[0072] Furthermore, the data storage security of the meteorological private cloud information platform is analyzed in detail as follows:

[0073] The data transmission rate of the meteorological private cloud information platform at each storage time point is averaged to obtain the average data transmission speed of the meteorological private cloud information platform during the storage period.

[0074] According to the data transmission rate of the meteorological private cloud information platform at each storage time point, the data transmission rate change curve of the meteorological private cloud information platform is constructed, such as Figure 3 As shown, it is used to display the data transmission rate change trend of the meteorological private cloud information platform during the data storage process. The horizontal axis of the data transmission rate change line graph is the storage time point, the unit is seconds, and the vertical axis is the data transmission rate, the unit is gigabits per second.

[0075] The number of data backups of the meteorological private cloud information platform within the storage period is ratio-processed with the duration corresponding to the storage period to obtain the data backup frequency of the meteorological private cloud information platform within the storage period.

[0076] The data transmission rate, data backup frequency and data encryption strength are extracted from the meteorological cloud information database.

[0077] Specifically, the data storage security of the meteorological private cloud information platform is obtained by comprehensive analysis of the average data transmission speed of the meteorological private cloud information platform during the storage period, the data backup frequency of the meteorological private cloud information platform during the storage period, the data encryption strength value of the meteorological private cloud information platform, the data transmission limit rate, the data backup limit frequency and the data encryption strength limit value, which is used to determine the data storage security of the meteorological private cloud information platform. In this embodiment, a more accurate calculation method is used to obtain it, and the specific expression is:

[0078]

[0079] Wherein B is the data storage security of the meteorological private cloud information platform. In this embodiment, if the data storage security of the meteorological private cloud information platform is to be ensured to be at a high level, it is necessary to consider the impact of the data transmission rate, data backup frequency and data encryption strength value of the meteorological private cloud information platform on data storage security; when the data transmission rate of the meteorological private cloud information platform is low, it will take longer to back up a large amount of data, thereby affecting the backup efficiency, thereby reducing the data storage security of the meteorological private cloud information platform; and data encryption will also affect the backup process. When the data encryption strength value is high, it may be necessary to appropriately reduce the backup frequency to reduce the impact on the performance of the security assessment system, thereby reducing the data backup frequency of the meteorological private cloud information platform, thereby reducing the data storage security of the meteorological private cloud information platform.

[0080] V is the average data transmission speed of the meteorological private cloud information platform during the storage period, which refers to the average rate at which data is transmitted from the processing end to the storage end of the meteorological private cloud information platform during the storage period.

[0081] V' is the data transmission limit rate, which refers to the minimum value of the specified data transmission rate.

[0082] P is the data backup frequency of the meteorological private cloud information platform during the storage period, which refers to the number of regular data backup operations during the data storage process of the meteorological private cloud information platform in order to ensure the security and recoverability of the data.

[0083] P' is the data backup frequency, which refers to the minimum value of the specified data backup frequency.

[0084] Q is the data encryption strength value of the meteorological private cloud information platform, which refers to the security performance or complexity of the encryption algorithm used by the meteorological private cloud information platform to protect data. This value is usually used to measure the difficulty of cracking data during the encryption process and is one of the important indicators for evaluating data security.

[0085] The above data encryption strength value can be determined by methods such as key length, algorithm complexity, and encryption and decryption speed. In this embodiment, the value is obtained specifically, and the determination of the data encryption strength is not paid too much attention.

[0086] Q' is the data encryption strength limit value, which refers to the minimum value of the specified data encryption strength value.

[0087] t1 is the correction factor corresponding to the data storage security predefined in the meteorological cloud information database.

[0088] In a specific embodiment, a mapping set of data storage security and its corresponding correction factor is constructed based on the relationship between historical data transmission average speed, historical data backup frequency, historical data encryption strength value and the data storage security of the meteorological private cloud information platform, and the real-time data transmission average speed, data backup frequency, and data encryption strength value are input into the mapping set to obtain the correction factor corresponding to the data storage security. At the same time, its value range in this example is (0, 1).

[0089] In this embodiment, the correction factor corresponding to the data storage security is set to 0.33, the data transmission limit rate is set to 15 gigabits per second, the data backup limit frequency is set to 30 percent, and the data encryption strength limit value is set to 5.

[0090] In this embodiment, the data storage security of the above-mentioned meteorological private cloud information platform is as shown in Table 2:

[0091] Table 2 Relationship between data storage security and its parameters

[0092]

[0093] It should be explained that the data in the above table are data of different dimensions and have different units and magnitudes. Therefore, in this embodiment, only the specific values ​​of each parameter are extracted to form the above expression for matrix multiplication. By using a normalized data processing method for the data in the above table, the dimensional differences and weight imbalance problems between different features are eliminated, thereby improving the convergence speed and accuracy of the above expression.

[0094] In this embodiment, according to the table of the data storage security of the above-mentioned meteorological private cloud information platform, when the average data transmission speed, data backup frequency and data encryption strength value of the meteorological private cloud information platform during the storage period are on an increasing trend and are greater than the preset limit value, the data storage security of the meteorological private cloud information platform will also increase. Therefore, if the data storage security of the meteorological private cloud information platform is to be improved, it is necessary to analyze the average data transmission speed, data backup frequency and data encryption strength value of the meteorological private cloud information platform during the storage period to reduce the negative impact caused by the mutual influence between the parameters.

[0095] Security assessment: Compare the data storage security of the meteorological private cloud information platform with the preset data storage security threshold, so as to provide early warning for the storage security of the meteorological private cloud information platform and finally complete the security assessment of meteorological data.

[0096] Furthermore, the safety assessment of the meteorological data is completed, and the specific analysis process is as follows:

[0097] The data storage security of the meteorological private cloud information platform is compared with the data storage security threshold preset in the meteorological cloud information database. If the data storage security of the meteorological private cloud information platform is less than the data storage security threshold, an early warning will be issued for the storage security of the meteorological private cloud information platform to complete the security assessment of the meteorological data.

[0098] The specific process of the above-mentioned storage security warning prompts for the meteorological private cloud information platform is: deploy a security warning mechanism in the meteorological private cloud information platform. When the security warning mechanism identifies that the data storage security of the meteorological private cloud information platform is less than the data storage security threshold, it automatically generates warning information, including key elements such as data risk type, impact scope, urgency, and recommended measures. The warning information is promptly notified to data processing monitoring personnel through multiple channels such as email, text messages, and instant messaging tools.

[0099] The above-mentioned security warning mechanism receives data storage information in the meteorological private cloud information platform, accurately understands the data storage situation in the meteorological private cloud information platform, issues a warning on the data storage situation, and then solves the data storage security problem in the meteorological private cloud information platform, thereby improving the data storage security in the meteorological private cloud information platform.

[0100] If the data storage security of the meteorological private cloud information platform is greater than or equal to the data storage security threshold, it means that the meteorological private cloud information platform has reached the preset security standards in terms of data storage and can better protect the confidentiality, integrity and availability of the data.

[0101] Reference Figure 2 As shown, the second aspect of the present invention provides a system for a meteorological private cloud information security assessment method based on a distributed architecture, including: a data acquisition module, a data processing module, a data storage module, and a security assessment module.

[0102] The second aspect of the present invention provides a system for a meteorological private cloud information security assessment method based on a distributed architecture, which also includes: a meteorological cloud information database, which is used to store the suitable operating humidity of the collection equipment, wind speed collection reference difference, data transmission limit rate, data backup limit frequency, data encryption strength limit value and preset values ​​of various factors.

[0103] The data acquisition module is connected to the data processing module, the data processing module is connected to the data storage module, the data storage module is connected to the safety assessment module, and the data acquisition module and the data storage module are both connected to the meteorological cloud information library.

[0104] The data acquisition module is used to monitor the data acquisition process of the acquisition device and evaluate the data acquisition safety of the acquisition device. If the data acquisition safety of the acquisition device is greater than or equal to a preset data acquisition safety threshold, a data processing method for the meteorological data is matched.

[0105] The data processing module is used to obtain and analyze data processing information of meteorological data based on the data processing process of meteorological data, and obtain meteorological data storage categories through matching on the meteorological private cloud information platform.

[0106] The data storage module is used to store meteorological data in the meteorological private cloud information platform through meteorological data storage categories, obtain data storage related information, and evaluate the data storage security of the meteorological private cloud information platform.

[0107] The security assessment module is used to compare the data storage security of the meteorological private cloud information platform with a preset data storage security threshold, thereby providing early warning for the storage security of the meteorological private cloud information platform and ultimately completing the security assessment of the meteorological data.

[0108] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A meteorological private cloud information security assessment method based on a distributed architecture, characterized in that: include: Data acquisition: monitor the data acquisition process of the acquisition equipment and evaluate the data acquisition security of the acquisition equipment. If the data acquisition security of the acquisition equipment is greater than or equal to the preset data acquisition security threshold, the data processing method of the meteorological data is matched; Data processing: Based on the data processing process of meteorological data, obtain and analyze the data processing information of meteorological data, and obtain the meteorological data storage category through matching through the meteorological private cloud information platform; Data storage: store meteorological data in the meteorological private cloud information platform through the meteorological data storage category, obtain data storage related information, and evaluate the data storage security of the meteorological private cloud information platform; Security assessment: Compare the data storage security of the meteorological private cloud information platform with the preset data storage security threshold, so as to provide early warning for the storage security of the meteorological private cloud information platform and finally complete the security assessment of meteorological data; The data acquisition security of the acquisition device is analyzed in detail as follows: By monitoring the data acquisition process of the acquisition device, the data acquisition related information of the acquisition device is acquired; The data acquisition related information of the acquisition device specifically includes the ambient humidity of the data acquisition area at each acquisition time point, the wind speed collected by the acquisition device at each acquisition time point, and the number of acquisition errors of the acquisition device within the acquisition cycle; The ambient humidity of the data acquisition area at each acquisition time point is processed with the preset suitable operating humidity of the acquisition equipment to obtain the humidity deviation value of the data acquisition area at each acquisition time point, and the humidity deviation value ranked first is recorded as the maximum humidity deviation value of the data acquisition area during the acquisition period; The actual wind speed of the data acquisition area at each acquisition time point is obtained, and the difference processing is performed with the wind speed collected by the acquisition device at each acquisition time point to obtain the wind speed collection difference of the acquisition device at each acquisition time point, and the ratio processing is performed with the predefined wind speed collection reference difference to obtain the collection accuracy of the collection device at each acquisition time point; The reference difference of suitable operating humidity and wind speed for the collection equipment is extracted from the meteorological cloud information database.

2. The meteorological private cloud information security assessment method based on distributed architecture according to claim 1 is characterized by: The safety assessment of meteorological data is completed, and the specific analysis process is as follows: The data storage security of the meteorological private cloud information platform is compared with the preset data storage security threshold. If the data storage security of the meteorological private cloud information platform is less than the data storage security threshold, an early warning prompt will be issued for the storage security of the meteorological private cloud information platform to complete the security assessment of the meteorological data.

3. The meteorological private cloud information security assessment method based on distributed architecture according to claim 1 is characterized by: The data storage related information specifically includes the data transmission rate of the meteorological private cloud information platform at each storage time point, the number of data backups of the meteorological private cloud information platform within the storage period, and the data encryption strength value of the meteorological private cloud information platform.

4. The meteorological private cloud information security assessment method based on distributed architecture according to claim 2 is characterized by: The data storage security of the meteorological private cloud information platform is analyzed in detail as follows: The data transmission rate of the meteorological private cloud information platform at each storage time point is averaged to obtain the average data transmission rate of the meteorological private cloud information platform during the storage period; The number of data backups of the meteorological private cloud information platform within the storage period is processed by ratio with the duration corresponding to the storage period to obtain the data backup frequency of the meteorological private cloud information platform within the storage period; The data transmission rate, data backup frequency and data encryption strength are extracted from the meteorological cloud information database.

5. The method for assessing meteorological private cloud information security based on a distributed architecture according to claim 4 is characterized in that: The data storage security of the meteorological private cloud information platform is specifically expressed as: Where B is the data storage security of the meteorological private cloud information platform, V is the average data transmission speed of the meteorological private cloud information platform during the storage period, V' is the data transmission limit rate, P is the data backup frequency of the meteorological private cloud information platform during the storage period, P' is the data backup limit frequency, Q is the data encryption strength value of the meteorological private cloud information platform, Q' is the data encryption strength limit value, and t1 is the correction factor corresponding to the data storage security predefined in the meteorological cloud information database.

6. The method for evaluating the information security of a meteorological private cloud based on a distributed architecture according to claim 1 is characterized in that: The matching obtains the meteorological data storage category, and the specific analysis process is as follows: The data processing information of the meteorological data includes performing basic calculations on each key data point in the meteorological data to obtain a data processing set of the meteorological data; The data processing set of meteorological data is matched with the data storage categories corresponding to each data processing set predefined in the meteorological private cloud information platform, so as to obtain the meteorological data storage category.

7. The meteorological private cloud information security assessment method based on distributed architecture according to claim 1 is characterized by: The data processing method for matching the meteorological data, the specific matching process is: The data acquisition safety degree of the collection device is compared with the preset data acquisition safety threshold. If the data acquisition safety degree of the collection device is greater than or equal to the data acquisition safety threshold, the data acquisition safety degree of the collection device is matched with the data processing method corresponding to each data acquisition safety interval, so as to match the data processing method of the meteorological data collected in the collection device.

8. The method for assessing meteorological private cloud information security based on a distributed architecture according to claim 1 is characterized by: The data acquisition security of the acquisition device is specifically expressed as: Where A is the data acquisition safety of the acquisition equipment, ΔW is the maximum humidity deviation value of the data acquisition area during the acquisition cycle, and ZQ i is the collection accuracy of the collection device at each acquisition time point, i represents the number of each acquisition time point, i=1,3,4,...,m, m is the total number of acquisition time points, CS is the number of collection errors of the collection device within the acquisition cycle, D1 is the impact factor corresponding to the unit value of the maximum humidity deviation value predefined in the meteorological cloud information database, D2 is the impact factor corresponding to the unit value of the collection accuracy predefined in the meteorological cloud information database, and D3 is the impact factor corresponding to a single collection error predefined in the meteorological cloud information database.

9. A system using the meteorological private cloud information security assessment method based on a distributed architecture as described in any one of claims 1 to 8, characterized in that: include: The data acquisition module is used to monitor the data acquisition process of the acquisition device and evaluate the data acquisition safety of the acquisition device. If the data acquisition safety of the acquisition device is greater than or equal to the preset data acquisition safety threshold, the data processing method of the meteorological data is matched; The data processing module is used to obtain and analyze the data processing information of meteorological data based on the data processing process of meteorological data, and obtain the meteorological data storage category through matching on the meteorological private cloud information platform; The data storage module is used to store meteorological data in the meteorological private cloud information platform through the meteorological data storage category, obtain data storage related information, and evaluate the data storage security of the meteorological private cloud information platform; The security assessment module is used to compare the data storage security of the meteorological private cloud information platform with the preset data storage security threshold, so as to provide early warning for the storage security of the meteorological private cloud information platform and finally complete the security assessment of meteorological data.

Images